Study of physicochemical parameters and wetland water quality assessment by using Shannon’s entropy
Bibliographic record
Abstract
Abstract In water quality monitoring programs, optimization between information craved and information collected involves scrupulous judgment making processes and management approaches. The present study explores the few essential aspects of water quality monitoring program considering Shannon’s entropy with case studies on a few lakes and wetlands in North Guwahati, Assam (India). Firstly, the loss of information by traditional water quality indices (WQIs) has been addressed by the use of entropy weighted WQIs (EWQIs) which takes into account the randomness of data sets removing error through subjective judgments of experts in assigning parameter weights. This concept was extended to the quantification of heavy metals. The concept of multi-criteria decision-making methods (MCDMs) such as TOPSIS was introduced which utilize entropy weights and rough set theory to give a reliable and unbiased description of overall pollution levels of each sampling location. This study will be of great help to various agencies which take care of the water supply and water pollution control since this forms a significant tool for easy understanding and thereby making their applicability uncomplicated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".